---
id: apache-airflow-providers-presto
version: "5.12.1"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# apache-airflow-providers-presto — Provider package apache-airflow-providers-presto for Apache Airflow

License: permissive · Maintenance: active · Downloads: 240.8K/mo

## What it is and what it does

This is an Apache Airflow provider package that adds Presto database connectivity to Airflow DAGs. It wraps the presto-python-client library and integrates with Airflow's task execution model, allowing you to define SQL queries as Airflow operators that run on a schedule or in response to upstream task completion. The package depends on apache-airflow, apache-airflow-providers-common-sql for shared SQL abstractions, pandas for result handling, and psycopg2-binary for PostgreSQL compatibility in certain configurations.

Typically used in data pipeline orchestration where you need to execute analytical or ETL queries against a Presto cluster as part of a larger workflow. The provider handles connection management, query execution, and result retrieval, abstracting away direct client library calls. It supports modern Python versions (3.10–3.14) and is actively maintained by the Apache Airflow project.

Use it for:

- Schedule daily or hourly SQL aggregations on a Presto cluster as part of a data warehouse refresh pipeline.
- Trigger Presto queries conditionally based on upstream task results in a multi-step ETL workflow.
- Extract query results into pandas DataFrames for downstream Python processing or file export.
- Monitor Presto query execution and retry failed queries using Airflow's built-in retry and alerting mechanisms.
- Integrate Presto queries with Google Cloud Storage or other cloud providers via optional cross-provider dependencies.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Integrates Presto query execution into Apache Airflow workflows, enabling scheduled and triggered SQL queries against Presto clusters with result handling via pandas DataFrames.

Yes. This package is actively maintained, has no known vulnerabilities, installs with low friction, and is the standard way to run Presto queries in Airflow. Install it if you are already running Airflow and need to orchestrate Presto workloads; it is production-ready and widely used.

## Install

pip install apache-airflow-providers-presto
uv add apache-airflow-providers-presto
poetry add apache-airflow-providers-presto

## Installing apache-airflow-providers-presto

Before you install: Low friction install as a pure Python wheel. Actively maintained with a release 6 days old. Requires Apache Airflow 2.11.0 or later and modern Python (3.10–3.14); all runtime dependencies are stable, widely-used packages.

License in practice: Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions; you must retain license notices and provide a copy of the license.

Quickstart:

pip install apache-airflow-providers-presto

from airflow.providers.presto.operators.presto import PrestoOperator

task = PrestoOperator(
    task_id='query_presto',
    sql='SELECT * FROM my_table',
    presto_conn_id='presto_default'
)

Requires an existing Apache Airflow installation (>=2.11.0) and a configured Presto connection in Airflow's connection backend.

Verify before relying:

- Whether the package includes hooks, sensors, or transfer operators beyond the base PrestoOperator.
- Support for Presto-specific features such as partitioned query execution or result caching.
- Whether optional sqlalchemy extra enables ORM-style query building.

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 240.8K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

airflow presto integration, presto sql operator airflow, schedule presto queries, airflow database provider, presto data pipeline, airflow sql execution, airflow-provider, data-orchestration, sql-integration

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